Many conventional web-based marketing techniques rely on a user's browsing history data to market to a user. For example, as a user shops for products online and/or purchases products, information regarding the browsed and purchased products is recorded and used to provide recommendations and other marketing materials to the user. Companies use the data to send emails, push notifications, and other marketing materials with recommended products to the user. The recommendations are usually based on similarity of meta-tags among products like categories or purchase data from other users.
Web-based marketing techniques often do not adequately identify products to recommend to users because the product recommendations are based on limited information about the users. A user's browser history, for example, provides little or no information regarding the environment in which the user will use a product and how the style and color of a recommended product will fit within that environment. For example, a chair recommended may be recommended to a user based on the user having browsed for chairs. However, the recommended chair may have a color or style that is ill-suited for the environment in which the user intends to use the chair. The chair may not fit well in the intended location or may not fit within the room's color scheme. Existing techniques do not provide users with product recommendations that account for the environment in which the user will use the product.